Farr Institute London Short Courses


Introduction to Quantitative Causal Inference

The formal approach to statistical methods that address causal questions, known as ‘causal inference’, is increasingly seen as the way forward for quantitative epidemiology, with the concepts and methods contained fast becoming vital tools in any quantitative researcher’s box.

In this one-day course, we will introduce the key foundational concepts and tools in causal inference (potential outcomes, directed acyclic graphs) before moving on to see the benefits of these when applied to the study of mediation, i.e. questions concerning causal mechanisms such as “how much of the effect of socio-economic status on breast cancer survival is mediated by adherence to screening?”.

Although the formal nature of the subject inevitably requires that some mathematical details be given, the emphasis here will be on the main concepts and their application in a few realistic examples.

Learning Objectives

The important concepts in formal causal inference thinking will be introduced, along with some practical examples of this thinking guiding the analysis of data.

Some of the methods introduced will be used in examples using Stata or R (participants will be able to choose which of the two packages to use in the practical sessions).

Outline Timetable

Time Activity Led by
09:00-09:30 Registration and coffee  
09:30-10:30 Introduction to causal concepts I: what is your causal question? Rhian Daniel
10:30-10:45 Coffee  
10:45-11:30 Practical: Introduction to causal concepts II: Assumptions and Directed Acyclic Graphs Rhian Daniel, Michail Katsoulis
11:30-12:45 Causal concepts Rhian Daniel
12:45-14:00 Lunch  
14:00-14:45 Causal mediation analysis I: estimands and methods Rhian Daniel
14:45-15:00 Coffee  
15:00-15:45 Causal mediation analysis II: examples Rhian Daniel
15:45-17:00 Practical: Causal mediation analysis Rhian Daniel, Michail Katsoulis

Course Team

Dr Rhian Daniel (Lead Tutor)
Dr Michail Katsoulis